Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8,537
datasets available to search
ShareScore release 0.9.0
Dataset results
8,537 results for “Openness”
Indicative distribution map for Ecosystem Functional Group FM1.3 Intermittently closed and open lakes and lagoons
<p>This archive contains indicative distribution maps and profiles for <strong>FM1.3 Intermittently closed and open lakes and lagoons</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Smarter open government data for Society 5.0: analysis of 51 OGD portals
<p>This dataset contains data collected during a study <a href="https://doi.org/10.3390/s21155204">"Smarter open government data for Society 5.0: are your open data smart enough"</a> (<em>Sensors</em>. 2021; 21(15):5204) conducted by Anastasija Nikiforova (University of Latvia).<br> It being made public both to act as supplementary data for "Smarter open government data for Society 5.0: are your open data smart enough" paper and in order for other researchers to use these data in their own work.</p> <p>The data in this dataset were collected in the result of the inspection of 60 countries and their OGD portals (total of 51 OGD portal in May 2021) to find out whether they meet the trends of Society 5.0 and Industry 4.0 obtained by conducting an analysis of relevant OGD portals.</p> <p>Each portal has been studied starting with a search for a data set of interest, i.e. “real-time”, “sensor” and “covid-19”, follwing by asking a list of additional questions.<br> These questions were formulated on the basis of combination of (1) crucial open (government) data-related aspects, including open data principles, success factors, recent studies on the topic, PSI Directive etc., (2) trends and features of Society 5.0 and Industry 4.0, (3) elements of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use Model (UTAUT).</p> <p>The method used belongs to typical / daily tasks of open data portals sometimes called “usability test” – keywords related to a research question are used to filter data sets, i.e. “real-time”, “real time” and “real time”, “sensor”, covid”, “covid-19”, “corona”, “coronavirus”, “virus”. In most cases, “real-time”, “sensor” and “covid” keywords were sufficient.<br> The examination of the respective aspects for less user-friendly portals was adapted to particular case based on the portal or data set specifics, by checking:<br> 1. are the open data related to the topic under question ({sensor; real-time; Covid-19}) published, i.e. available?<br> 2. are these data available in a machine-readable format?<br> 3. are these data current, i.e. regularly updated? Where the criteria on the currency depends on the nature of data, i.e. Covid-19 data on the number of cases per day is expected to be updated daily, which won’t be sufficient for real-time data as the title supposes etc.<br> 4. is API ensured for these data? having most importance for real-time and sensor data;<br> 5. have they been published in a timely manner? which was verified mainly for Covid-19 related data. The timeliness is assessed by comparing the dates of the first case identified in a given country and the first release of open data on this topic.<br> 6. what is the total number of available data sets?<br> 7. does the open government data portal provides use-cases / showcases? <br> 8. does the open government portal provide an opportunity to gain insight into the popularity of the data, i.e. does the portal provide statistics of this nature, such as the number of views, downloads, reuses, rating etc.?<br> 9. is there an opportunity to provide a feedback, comment, suggestion or complaint?<br> 10. (9a) is the artifact, i.e. feedback, comment, suggestion or complaint, visible to other users?</p> <p>***Format of the file***<br> .xls, .ods, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal. <em>Journal of Affective Disorders</em>. </strong></p> <p><em>Background:</em> To investigate the powerful placebo effects in antidepressant drug trials and their mechanisms, recent pioneering experimental studies showed that expectation manipulation combined with an active placebo attenuated induced sadness. In the present study, we aimed at extending these findings by assessing the psychophysiological response in addition to mere self-report.</p> <p><em>Methods:</em> One hundred thirteen healthy female students were randomly assigned to a drug expectation group (active placebo, positive treatment expectation), placebo expectation group (active placebo, no treatment expectation), or a no-treatment group (no placebo, no treatment expectation). After placebo intake, sadness was induced by self-deprecating statements using the Velten method combined with sad music, including a rumination phase. Sadness was measured using the Positive and Negative Affect Schedule Expanded Form (PANAS-X). Heart rate and skin conductance were assessed continuously.</p> <p><em>Results:</em> After mood induction and after rumination, self-reported sadness was significantly lower, and skin conductance level was significantly higher, in the drug expectation group than in the no-treatment group. The mood induction was further accompanied by a heart rate deceleration within all groups.</p> <p><em>Limitations: </em>Generalizability is limited by sample selectivity and focusing on sadness as a symptom of depression, exclusively.</p> <p><em>Conclusion:</em> Expectation-induced placebo effects significantly influenced sadness-correlated changes in autonomic arousal, and not only subjectively reported sadness, indicating that placebo effects in the context of affect are not merely due to subjective response bias. The systematic modification of treatment expectation could be utilized in clinical practice to optimize current therapeutic approaches to improve mood regulation.</p>
COCONUT: the COlleCtion of Open NatUral producTs.
<p>COCONUT is a COlleCtion of Open NatUral producTs.</p> <p> </p> <p>The database is now available at <a href="https://coconut.naturalproducts.net/">coconut.naturalproducts.net</a>, where the latest updates will appear before being available here.</p> <p>To assemble COCONUT, data from 55 open access collections and databases of natural products was retrieved and curated.</p> <p>This archive contains two files:</p> <ul> <li>The MongoDB dump, the most complete version of the dataset, with extensive molecular annotations</li> <li>The COCONUT4MetFrag file, used for <a href="https://msbi.ipb-halle.de/MetFrag/">MetFrag</a>. The last version of COCONUT4MetFrag is in the file "COCONUT4MetFrag_april.csv"</li> <li>The COCONUT.sdf file containing all unique NP molecules with selected metadata</li> </ul> <p>To restore the dataset in MongoDB:</p> <pre><code class="language-bash">unzip COCONUT_2021_03.zip cd COCONUT_2021_03/COCONUT_2021_03/ mongorestore --db=COCONUT --noIndexRestore . </code></pre> <p>It is generally useful to avoid restoring indexes, as they can interfere with the local installation. Here are the commands to rebuild indexes:</p> <pre><code class="language-json">mongo use COCONUT db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) </code></pre> <p><br> <strong>This version of COCONUT is beta and will be curated further, but can already be used as it is.</strong></p>
Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets
<p>This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).</p> <p><strong>DATA ORIGINS & LICENSE INFORMATION</strong></p> <p>The data comes from four existing open datasets collected by others:</p> <p>Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants</p> <ul> <li>article: https://www.nature.com/articles/s41597-019-0124-4</li> <li>dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767</li> </ul> <p>Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals</p> <ul> <li>article: https://peerj.com/articles/4640/</li> <li>dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711</li> </ul> <p>Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals</p> <ul> <li>article: https://www.nature.com/articles/s41598-019-38748-8</li> <li>dataset: https://data.mendeley.com/datasets/svx74xcrjr/3</li> </ul> <p>Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions</p> <ul> <li>article: https://www.sciencedirect.com/science/article/pii/S0021929021001007</li> <li>dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1</li> </ul> <p>In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.<br> The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).</p> <p>We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim).<br> In this dataset, we publish modified data as well as some of the original data.</p> <p><br> <strong>STRUCTURE OF THE DATASET</strong><br> The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".</p> <p>Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.<br> Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.<br> The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them.<br> X can be:<br> - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments<br> - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment<br> - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment<br> - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments<br> - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment<br> - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment<br> - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments<br> - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment<br> - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment<br> - MFR_common: the medial force ratio for the entire stance phase<br> - MFR_LR: the medial force ratio for the first peak of stance<br> - MFR_TE: the medial force ratio for the second peak of stance</p> <p>The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.<br> The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.</p> <ul> <li>DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}</li> <li>SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").</li> <li>KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.</li> <li>MASS is the mass of the subject in kilograms</li> <li>HEIGHT is the height of the subject in millimeters</li> <li>BMI is the body mass index of the subject</li> <li>WALKING_SPEED is the mean walking speed of the subject during the trial</li> <li>HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike</li> <li>AGE is the age of the subject in years</li> <li>GENDER is an integer/boolean where {1=male, 0=female}</li> </ul> <p>The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.<br> The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).</p> <p><br> See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.</p> <p>Questions & other contacts: jere.lavikainen@uef.fi</p>
Stories on Open Educational Practices in German Higher Education
<p><strong>Stories on Open Educational Practices in German Higher Education by Sigrid Fahrer, </strong><a href="#_oewao57q7xt8"><strong>Tamara Heck</strong></a><strong>, </strong><a href="#_fronuh2cauex"><strong>Ronny Röwert</strong></a><strong>, </strong><a href="#_oshco7u6xx6k"><strong>Naomi Truan</strong></a></p> <p><em>citation suggestion: </em>Fahrer, S., Heck, T., Röwert, R., Truan, N. (2022). Stories on Open Educational Practices in German Higher Education. Data set on autoethnographic reflections. <a href="https://doi.org/10.5281/zenodo.7326390">https://doi.org/10.5281/zenodo.7326390</a></p> <p>The stories are part of the autoethnographic reflections of the four practitioners. They are based on the following research papers:</p> <ul> <li>Cronin, C. (2017). Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education. The International Review of Research in Open and Distributed Learning, 18(5). <a href="https://doi.org/10.19173/irrodl.v18i5.3096">https://doi.org/10.19173/irrodl.v18i5.3096</a></li> <li>Hegarty, B. (2015). Attributes of Open Pedagogy: A Model for Using Open Educational Resources. Educational Technology, 55(4), 3–13. <a href="https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf">https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf</a></li> <li>Mayrberger, K. (2020). Open Educational Practices (OEP) in Higher Education. In M. A. Peters (Ed.), Springer eBook Collection. Encyclopedia of Educational Philosophy and Theory (pp. 1–7). Springer. <a href="https://doi.org/10.1007/978-981-287-532-7_710-1">https://doi.org/10.1007/978-981-287-532-7_710-1</a>.</li> <li>Wiley, D., & Hilton III, J. L. (2018). Defining OER-Enabled Pedagogy. The International Review of Research in Open and Distributed Learning, 19(4). <a href="https://doi.org/10.19173/irrodl.v19i4.3601">https://doi.org/10.19173/irrodl.v19i4.3601</a></li> </ul>
QST - open data of the article Vanwindekens & Hardy (2022) - table 1 soil properties
<p>Soil properties of the long term fields trials linked to the paper "The QuantiSlakeTest, dynamic weighting of soil under water to measure soil structural stability" submitted to the SOIL journal by Vanwindekens & Hardy (2022).</p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
Open Soil Spectral Library (training data and calibration models)
<p><strong>Open Soil Spectral Library</strong> contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set:</p> <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> <p>Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe.</p> <p>To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <strong><a href="https://github.com/traversc/qs">QS data format</a></strong> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a></p> <p>Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a></p> <p><a href="https://soilspectroscopy.org/"><strong>Soil Spectroscopy for the Global Good</strong></a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>).</p> <p>Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>. </p> <p>For more advanced uses of the soil spectral libraries <strong>we advise to contact the original data producers</strong> especially to get help with using, extending and improving the original SSL data.</p>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Data from "PathOS - D1.2 Scoping Review of Open Science Impact"
<p>This dataset contains the data from the Scoping Review of Open Science Impact. Included are all record for which we assessed the full-text.</p> <p>The columns are as follows:</p> <ul> <li>id: Internal identifier</li> <li>Several metadata columns from Scopus/Web of Science: authors, year, title, abstract, type, DOI</li> <li>OS type: type of Open Science (e.g., Open Access, Citizen Science)</li> <li>inclusion_status: included, duplicate, out of scope, non-english</li> <li>justification: reasons for decision on inclusion_status</li> <li>Several columns with data extracted by the authors: Study details and design, Types of data sources, Study aims, Relevance to which aspect of impact, Key findings, Coverage/Context, Confidence assessment</li> </ul> <p>A complete description of the methods and detailed instructions for coders for extracting data from reports is contained in section 2 of the deliverable report which is available at <a href="https://doi.org/10.5281/zenodo.7883699">https://doi.org/10.5281/zenodo.7883699</a>.</p>
EMB3Rs Open digital research data
<p>The EMB3Rs Unified Modelling Platform is a tool to assist on modelling the recovery of excess heat and its reuse to meet final energy demand within and beyond the boundaries of industrial sites. The tool consists of a knowledge base and several simulation modules.<br> This database comprises the research data generated in the course of the EMB3Rs project by using the EMB3Rs platform.<br> The pdf file contains the detailed description of the database content</p> <p>It has been deposited at Zenodo’s open data repository with DOI 10.5281/zenodo.7994255.</p>
Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.
<p><strong>ABSTRACT </strong></p> <p>It contains the data of drug targets (gene names), uniprot identifiers, secondary linked data sources (e.g., PharmGKB), market drug name, chembl identifier, and pubchem compound identifier obtained from DGIdb.</p> <p><strong>Instructions: </strong></p> <p>Data were cleaned and duplicates were removed. Data were all categorical features.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project. It is used for constructing R2G dataset, which will map drugs to their drug targets (gene -> protein = drug target)</p> <p><strong>Acknowledgements</strong></p> <p>Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151. doi: 10.1093/nar/gkaa1084. PMID: 33237278; PMCID: PMC7778926.</p> <p>https://www.dgidb.org/</p> <p><strong>U-BRITE last update date:</strong> 06/09/2023</p>
Stakeholders in Croatian Agriculture Open Data Ecosystem
<p>The dataset shows the identified stakeholders in the Croatian agriculture open data ecosystem in the researched literature available on national portals Hrčak (the Portal of Croatian scientific and professional journals) and Dabar (Digital Academic Archives and Repositories). The complex query: "stakeholder" OR "persons" OR "actors" OR "agriculture" OR "agriculture business" OR "farms" OR "agriculture sector" OR "agriculture area" OR "agriculture field" AND "open data" was used for search of the national databases Hrčak and Dabar. The stakeholders identified in the query were classified and grouped into five key stakeholder groups: Agriculture producers/Farmers, Suppliers, Management and Support Organizations, Consumer Organizations/Consumers, Researches and Scientists, and Others.</p>
Workflow for detecting biomedical articles with openly available underlying datasets - Datasets and extraction forms
<p>The open data screening datasets contain both automatically detected (TRUE) Open Data statements by <a href="https://github.com/quest-bih/oddpub">ODDPub</a>, and its manual validation using <a href="https://github.com/bgcarlisle/Numbat">Numbat</a> extraction tool. Furthermore, extraction forms for both screenings – 2020 and 2021 – are included. The manually processed dataset for the calculation of the inter-rater reliability of manual validation can be also found here. </p> <p>(i) Data from articles published in 2020 (file ‘<em>charite_open_data_2020.csv</em>’) have been collected applying a slightly different sequence of questions in the extraction workflow than the articles published in 2021 (file ‘<em>charite_open_data_2021.csv</em>’). Both datasets were cleaned for any personal data or internal comments. Thus, they do not contain the default columns which in the raw export from Numbat contained commentaries regarding different question. Also, in another regard these files do not represent raw outputs of the Numbat extraction tool, but a processed version. This means that articles validated by more than two raters were first reconciled in Numbat, resulting in one final decision (output of extractions <strong>after reconciliation</strong>). Then from the output of extractions <strong>before reconciliation</strong> those articles validated by only 1 rater (and thus not part of the inter-rater reliability calculation) were selected, which were afterwards joined with the already reconciled dataset. </p> <p>The actual decision about Openness of validated dataset can be analysed in various ways: </p> <ol> <li>Column ‘<em>open_data_assessment</em>’/’<em>assessment</em>’ shows a binary decision between Open Data TRUE and FALSE. </li> <li>If that column indicates ‘<em>NULL</em>’, the dataset was classified into ‘non’-open category, and the result can be found on one of the following ways: <ul> <li>Column ‘<em>reference_to_data</em>’ as ‘<em>n_a</em>’ for excluded articles, e.g. not producing any data.</li> <li>Column ‘<em>data_access</em>’ as ‘<em>restricted</em>’. </li> <li>Column ‘<em>own_or_reuse_data</em>’ as ‘<em>open_data_reuse</em>’. </li> </ul> </li> </ol> <p>The original extraction form contains an option ‘unsure_open_data’ besides ‘<em>open_data</em>’/’<em>no_open_data</em>’ which was resolved either during reconciliation between multiple raters or by case-related consultation with a second rater in case of doubt, and is not included here. </p> <p>(ii) The inter-rater reliability calculation was made on randomly selected 100 articles for 2 raters. The third rater screened 20 articles sample, which is part of 100 sample. The tables provided here include both article-level data, and dataset-level data. </p> <p>(iii) The Numbat extarction forms used for the screenings in 2020 and 2021 are included in two formats - JSON and Markdown.</p> <p>(iv) ‘<em>data_dictionary_open_data.csv</em>’ table documents all variables of each data file containing here. </p>
The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.</p> <p>The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.</p> <p>The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.</p> <p><strong>Accessing original data source</strong></p> <p>Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state;</p> <p>c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.</p> <p><strong>Proceeding to data analysis</strong></p> <p>We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).</p> <p>The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.</p> <p>a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;</p> <p>d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;</p> <p>e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;</p> <p>f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.</p> <p>Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.</p> <p>For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.</p>
OneNet Portuguese demonstration - Open Data sets
<p>File containing the open data sets from the Portuguese demonstration of the OneNet project. The file includes the flexibility assets data used for the demonstration, as well as: 1) the data series for the estimation of the accumulated flexibility potential of MV customers (supermarkets) connected at the two substations considered; 2) the consumption and generation forecasts, with generation disaggregated by source; 3) short-circuit current values calculated at the EHV/HV interface level, including the TSO contribution, the DSO contribution and the joint TSO-DSO contribution. </p><p>Scope/objective of the demonstration: Test an optimized procedure for data exchange between the Portuguese DSO and TSO for flexibility and operational planning purposes.</p>
OpenITI: a Machine-Readable Corpus of Islamicate Texts
<p><strong>Co-PIs</strong>: Matthew Thomas Miller (University of Maryland, College Park), Maxim G. Romanov (University of Hamburg), Sarah Bowen Savant (Aga Khan University—ISMC, London).</p> <p><em>Open Islamicate Texts Initiative</em> (<strong>OpenITI</strong>, see <a href="https://openiti.org/">https://openiti.org/</a>) is a multi-institutional effort to construct the first machine-actionable scholarly corpus of premodern Islamicate texts. Led by researchers at the Aga Khan University, Institute for the Study of Muslim Civilisations (AKU-ISMC), University of Hamburg (UH), and the Roshan Institute for Persian Studies at the University of Maryland (College Park) and an interdisciplinary advisory board of leading digital humanists and Islamic, Persian, and Arabic studies scholars, <strong>OpenITI</strong> aims to provide the essential textual infrastructure in Arabic, Persian and other Islamicate languages for new forms of textual analysis and digital scholarship. In the process, OpenITI will enable new synergies between Digital Humanities and the inter-related Islamicate fields of Islamic, Persian, and Arabic Studies. In addition to support from the researchers’ home institutions, it is supported by funding from the <a href="https://erc.europa.eu/">European Research Council</a> under the European Union’s Horizon 2020 research and innovation programme, awarded to the <a href="http://kitab-project.org/">KITAB</a> project (Grant Agreement No. 772989, PI Sarah Bowen Savant) and the <a href="https://www.qnl.qa/en">Qatar National Library</a>.</p> <p>Currently, <strong>OpenITI</strong> contains almost exclusively Arabic texts, which were first assembled into a corpus within the <strong>OpenArabic</strong> project, developed first at Tufts University (at <em>The Perseus Project</em>, 2013–2015) and then at Leipzig University (at the Alexander von Humboldt Chair for Digital Humanities, 2015–2017)—in both cases with the support and under the patronage of Prof. Gregory Crane. The much more limited number of Persian texts were compiled during 2015–2016 in the Persian Digital Library (PDL) pilot (see <a href="https://persdigumd.github.io/PDL/">Persian Digital Library by PersDigUMD</a>) at Roshan Institute for Persian Studies at the University of Maryland. These texts have not been made fully compatible with OpenITI mARkdown yet and will be made fully available in next releases.</p> <p>This release contains all digital versions of the same text that are available in the OpenITI corpus . <strong>We also release a <a href="https://doi.org/10.5281/zenodo.7764025">'primary' version of the corpus</a></strong> that contains a single digital version for each text in the corpus that is marked as 'PRI' in the corpus metadata and may be more convenient for some use cases.</p> <p><strong>Note on Release Numbering</strong>: Version <strong>2019.1.1</strong>—where <strong>2019</strong> is the year of the release, the first dotted number—<strong>.1</strong>—is the ordinal release number in 2019, and the second dotted number—<strong>.1</strong>—is the overall release number; the first dotted number will reset every year, while the second one will continue on increasing.</p> <p>For more details: <a href="https://github.com/OpenITI/RELEASE">https://github.com/OpenITI/RELEASE</a></p> <p><strong>Note: </strong>In case of any issues with unzipping the files on Windows using built-in utilities, please use free softwares, such as WinRAR and 7zip.</p> <p> </p>
Bird Communities in Fragmented, Non-Native Pine Plantations in the Oak Openings Region of Northwest Ohio
Comprehensive surveys, while preferred, are not always feasible due to time, logistical, and funding constraints. However, limited surveys of focal taxa, such as birds, coupled with vegetation surveys, can provide critical information to guide land management. In the 1930s non-native conifers were planted in the Oak Openings Region of northwestern Ohio, a biodiversity hotspot. The stands are declining, and management is needed, but restoration to native habitat is time consuming and expensive. Our research utilized an avian perspective of ecological function of introduced pine plantations versus native remnants to guide management. We surveyed bird activity May through July 2020 with point-counts in nine sites (1.3-2.3 ha) with three each of white pine, red pine, and oak forest sites. At each site, we estimated bird richness, abundance, and diversity, as well as structural characteristics (e.g., canopy cover), composition (e.g., vegetation types), and landscape context (e.g., landcover). Superficially, the pine sites appear to be beneficial as pine habitat for breeding birds, with high Simpson’s indices (up to 0.89) and high species richness compared to oak sites. However, our results reveal that the pines are not truly functioning as pine habitat for birds based on the limited occurrence of pine specialist species, proportion of generalists to pine specialists, and landscape context. Simple measures of diversity with no consideration as to species identity and without the environmental context fail to provide reliable measures of ecological value. Instead, we recommend selective sampling and consideration of landscape context, vegetation structure, and species classification to guide management.
Ferns surveys of individuals of terrestrial ferns in Canopy Trimming Experiment (CTE) plots document changes in species richness and abundance over time in response to canopy opening and/or debris deposition
Whole plot surveys of Canopy Triming Experiment (CTE) plots were done to detect changes in the number of terrestrial fern species and individuals in response to canopy opening and debris deposition. Surveys were conducted annually prior to and after treatments. A count of all terrestrial ferns, identified to species on the CTE plots was recorded for each subplot in January during CTE1 (2002-2010) and in the fall during CTE2 (2014-present). The surveys document losses of individuals of shade tolerant fern species and the appearance of open canopy ferns such as the tree fern Cyathea arborea. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.